자아 정체성, 국가와 성의 정치학: - 마거릿 앳우드의『수면 위로 떠오르기』 = Self-Identity, the Politics of Nation and Gender: Margaret Atwood’s <i>Surfacing</i>
Bibliographic record
Abstract
In Surfacing, Margaret Atwood, who is one of the most powerful writers in Canada, creates a nameless speaker and heroine in order to explore her identity as a woman and in a macro sense, as a nation. She accomplishes the whole process by the technique of using visual devices such as albums, pictures, video cameras, illustrations, and images in order to create a tool to use for the speaker’s job as an illustrator. First, she makes her heroine revise Canadian cultural myths and the official history of Canadian former settlers and re-evaluate all cultural assumptions and presuppositions on Canada and women. And she then causes her heroine to enter upon a quest for her self-identity, which has been fixed with in the stereotype of Western fashions, especially when it has related to the politics of the nation and the female gender. This indicates Atwood’s self-criticism of Canada and Canadians, in her hope to remake Canada as a nation, a culture, and a society and to help individuals, such as women, to find their proper identities, survive their attributed selves, and live their independent lives. Therefore, I can say that Surfacing is Atwood’s expression of love for Canada and of her dream for a hopeful future for all Canadians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.024 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".